AutoWeka: Towards an automated data mining software for QSAR and QSPR studies


Quantitative structure-activity relationship (QSAR) has been instrumental in aiding medicinal chemists and physical scientists in understanding how modification of substituents at different positions on a molecular structure exert its influence on the observed biological activity and physicochemical property, respectively. QSAR has received great attention owing to its predictive capability and as such had directed efforts toward obtaining models with high prediction performance. However, to be useful QSAR models need to be informative and interpretable in which the underlying molecular features that contribute to the increase or decrease of the biological activity are revealed by the model. Thus, the aim of this chapter is to briefly review the general concepts of QSAR modeling, its development and discussions on key issues influencing and contributing to the interpretability of QSAR models.

In Artificial Neural Networks: Methods and Applications
Nantasenamat C, Worachartcheewan A, Jamsak S, Preeyanon L, Shoombuatong W, Simeon S, Mandi P, Isarankura-Na-Ayudhya C, Prachayasittikul V. AutoWeka: Towards an Automated Data Mining Software for QSAR and QSPR Studies. In: Cartwright H, Artificial Neural Networks: Methods and Applications, 2nd ed., Methods in Molecular Biology 1260 (2015) 119-147, DOI: 10.1007/978-1-4939-2239-0_8, ISBN-13: 978-1-4939-2238-3.